{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "01_Keras_stateful_RNN_playground.ipynb",
      "version": "0.3.2",
      "provenance": [],
      "collapsed_sections": [],
      "toc_visible": true,
      "include_colab_link": true
    },
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/GoogleCloudPlatform/tensorflow-without-a-phd/blob/master/tensorflow-rnn-tutorial/01_Keras_stateful_RNN_playground.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "RH-br21Mfg83",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "# An stateful RNN model to generate sequences\n",
        "RNN models can generate long sequences based on past data. This can be used to predict stock markets, temperatures, traffic or sales data based on past patterns. They can also be adapted to [generate text](https://docs.google.com/presentation/d/18MiZndRCOxB7g-TcCl2EZOElS5udVaCuxnGznLnmOlE/pub?slide=id.g139650d17f_0_1185). The quality of the prediction will depend on training data, network architecture, hyperparameters, the distance in time at which you are predicting and so on. But most importantly, it will depend on wether your training data contains examples of the behaviour patterns you are trying to predict."
      ]
    },
    {
      "metadata": {
        "id": "9l96vOsPfg84",
        "colab_type": "code",
        "outputId": "a37152a6-fd37-405f-9627-774aa37357cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "import math\n",
        "import numpy as np\n",
        "from matplotlib import pyplot as plt\n",
        "import tensorflow as tf\n",
        "tf.enable_eager_execution()\n",
        "print(\"Tensorflow version: \" + tf.__version__)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Tensorflow version: 1.13.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "gxJ60M-4ilJy",
        "colab_type": "code",
        "cellView": "form",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "#@title Data formatting and display utilites [RUN ME]\n",
        "\n",
        "def dumb_minibatch_sequencer(data, batch_size, sequence_size, nb_epochs):\n",
        "    \"\"\"\n",
        "    Divides the data into batches of sequences in the simplest way: sequentially.\n",
        "    :param data: the training sequence\n",
        "    :param batch_size: the size of a training minibatch\n",
        "    :param sequence_size: the unroll size of the RNN\n",
        "    :param nb_epochs: number of epochs to train on\n",
        "    :return:\n",
        "        x: one batch of training sequences\n",
        "        y: one batch of target sequences, i.e. training sequences shifted by 1\n",
        "    \"\"\"\n",
        "    data_len = data.shape[0]\n",
        "    nb_batches = data_len // (batch_size * sequence_size)\n",
        "    rounded_size = nb_batches * batch_size * sequence_size\n",
        "    xdata = data[:rounded_size]\n",
        "    ydata = np.roll(data, -1)[:rounded_size]\n",
        "    xdata = np.reshape(xdata, [nb_batches, batch_size, sequence_size])\n",
        "    ydata = np.reshape(ydata, [nb_batches, batch_size, sequence_size])\n",
        "\n",
        "    for epoch in range(nb_epochs):\n",
        "        for batch in range(nb_batches):\n",
        "            yield xdata[batch,:,:], ydata[batch,:,:]\n",
        "            \n",
        "            \n",
        "def rnn_minibatch_sequencer(data, batch_size, sequence_size, nb_epochs):\n",
        "    \"\"\"\n",
        "    Divides the data into batches of sequences so that all the sequences in one batch\n",
        "    continue in the next batch. This is a generator that will keep returning batches\n",
        "    until the input data has been seen nb_epochs times. Sequences are continued even\n",
        "    between epochs, apart from one, the one corresponding to the end of data.\n",
        "    The remainder at the end of data that does not fit in an full batch is ignored.\n",
        "    :param data: the training sequence\n",
        "    :param batch_size: the size of a training minibatch\n",
        "    :param sequence_size: the unroll size of the RNN\n",
        "    :param nb_epochs: number of epochs to train on\n",
        "    :return:\n",
        "        x: one batch of training sequences\n",
        "        y: one batch of target sequences, i.e. training sequences shifted by 1\n",
        "    \"\"\"\n",
        "    data_len = data.shape[0]\n",
        "    # using (data_len-1) because we must provide for the sequence shifted by 1 too\n",
        "    nb_batches = (data_len - 1) // (batch_size * sequence_size)\n",
        "    assert nb_batches > 0, \"Not enough data, even for a single batch. Try using a smaller batch_size.\"\n",
        "    rounded_data_len = nb_batches * batch_size * sequence_size\n",
        "    xdata = np.reshape(data[0:rounded_data_len], [batch_size, nb_batches * sequence_size])\n",
        "    ydata = np.reshape(data[1:rounded_data_len + 1], [batch_size, nb_batches * sequence_size])\n",
        "\n",
        "    whole_epochs = math.floor(nb_epochs)\n",
        "    frac_epoch = nb_epochs - whole_epochs\n",
        "    last_nb_batch = math.floor(frac_epoch * nb_batches)\n",
        "    \n",
        "    for epoch in range(whole_epochs+1):\n",
        "        for batch in range(nb_batches if epoch < whole_epochs else last_nb_batch):\n",
        "            x = xdata[:, batch * sequence_size:(batch + 1) * sequence_size]\n",
        "            y = ydata[:, batch * sequence_size:(batch + 1) * sequence_size]\n",
        "            x = np.roll(x, -epoch, axis=0)  # to continue the sequence from epoch to epoch (do not reset rnn state!)\n",
        "            y = np.roll(y, -epoch, axis=0)\n",
        "            yield x, y\n",
        "            \n",
        "\n",
        "plt.rcParams['figure.figsize']=(16.8,6.0)\n",
        "plt.rcParams['axes.grid']=True\n",
        "plt.rcParams['axes.linewidth']=0\n",
        "plt.rcParams['grid.color']='#DDDDDD'\n",
        "plt.rcParams['axes.facecolor']='white'\n",
        "plt.rcParams['xtick.major.size']=0\n",
        "plt.rcParams['ytick.major.size']=0\n",
        "plt.rcParams['axes.titlesize']=15.0\n",
        "\n",
        "\n",
        "def display_lr(lr_schedule, nb_epochs):\n",
        "  x = np.arange(nb_epochs)\n",
        "  y = [lr_schedule(i) for i in x]\n",
        "  plt.figure(figsize=(9,5))\n",
        "  plt.plot(x,y)\n",
        "  plt.title(\"Learning rate schedule\\nmax={:.2e}, min={:.2e}\".format(np.max(y), np.min(y)),\n",
        "            y=0.85)\n",
        "  plt.show()\n",
        "  \n",
        "def display_loss(history, full_history, nb_epochs):\n",
        "  plt.figure()\n",
        "  plt.plot(np.arange(0, len(full_history['loss']))/steps_per_epoch, full_history['loss'], label='detailed loss')\n",
        "  plt.plot(np.arange(1, nb_epochs+1), history['loss'], color='red', linewidth=3, label='average loss per epoch')\n",
        "  plt.ylim(0,3*max(history['loss'][1:]))\n",
        "  plt.xlabel('EPOCH')\n",
        "  plt.ylabel('LOSS')\n",
        "  plt.xlim(0, nb_epochs+0.5)\n",
        "  plt.legend()\n",
        "  for epoch in range(nb_epochs//2+1):\n",
        "    plt.gca().axvspan(2*epoch, 2*epoch+1, alpha=0.05, color='grey')\n",
        "  plt.show()\n",
        "\n",
        "def picture_this_7(features):\n",
        "    subplot = 231\n",
        "    for i in range(6):\n",
        "        plt.subplot(subplot)\n",
        "        plt.plot(features[i])\n",
        "        subplot += 1\n",
        "    plt.show()\n",
        "    \n",
        "def picture_this_8(data, prime_data, results, offset, primelen, runlen, rmselen):\n",
        "    disp_data = data[offset:offset+primelen+runlen]\n",
        "    colors = plt.rcParams['axes.prop_cycle'].by_key()['color']\n",
        "    plt.subplot(211)\n",
        "    plt.xlim(0, disp_data.shape[0])\n",
        "    plt.text(primelen,2.5,\"DATA |\", color=colors[1], horizontalalignment=\"right\")\n",
        "    plt.text(primelen,2.5,\"| PREDICTED\", color=colors[0], horizontalalignment=\"left\")\n",
        "    displayresults = np.ma.array(np.concatenate((np.zeros([primelen]), results)))\n",
        "    displayresults = np.ma.masked_where(displayresults == 0, displayresults)\n",
        "    plt.plot(displayresults)\n",
        "    displaydata = np.ma.array(np.concatenate((prime_data, np.zeros([runlen]))))\n",
        "    displaydata = np.ma.masked_where(displaydata == 0, displaydata)\n",
        "    plt.plot(displaydata)\n",
        "    plt.subplot(212)\n",
        "    plt.xlim(0, disp_data.shape[0])\n",
        "    plt.text(primelen,2.5,\"DATA |\", color=colors[1], horizontalalignment=\"right\")\n",
        "    plt.text(primelen,2.5,\"| +PREDICTED\", color=colors[0], horizontalalignment=\"left\")\n",
        "    plt.plot(displayresults)\n",
        "    plt.plot(disp_data)\n",
        "    plt.axvspan(primelen, primelen+rmselen, color='grey', alpha=0.1, ymin=0.05, ymax=0.95)\n",
        "    plt.show()\n",
        "\n",
        "    rmse = math.sqrt(np.mean((data[offset+primelen:offset+primelen+rmselen] - results[:rmselen])**2))\n",
        "    print(\"RMSE on {} predictions (shaded area): {}\".format(rmselen, rmse))"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ouNkUJLBfg89",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Generate fake dataset [WORK REQUIRED]\n",
        " * Pick a wavewform below: 0, 1 or 2. This will be your dataset."
      ]
    },
    {
      "metadata": {
        "id": "QLTqiqjSfg8-",
        "colab_type": "code",
        "outputId": "14dd77eb-c8a3-4636-9abe-3c4c6e5985d2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 375
        }
      },
      "cell_type": "code",
      "source": [
        "WAVEFORM_SELECT = 0 # select 0, 1 or 2\n",
        "\n",
        "def create_time_series(datalen):\n",
        "    # good waveforms\n",
        "    frequencies = [(0.2, 0.15), (0.35, 0.3), (0.6, 0.55)]\n",
        "    freq1, freq2 = frequencies[WAVEFORM_SELECT]\n",
        "    noise = [np.random.random()*0.1 for i in range(datalen)]\n",
        "    x1 = np.sin(np.arange(0,datalen) * freq1)  + noise\n",
        "    x2 = np.sin(np.arange(0,datalen) * freq2)  + noise\n",
        "    x = x1 + x2\n",
        "    return x.astype(np.float32)\n",
        "\n",
        "DATA_LEN = 1024*128+1\n",
        "data = create_time_series(DATA_LEN)\n",
        "plt.plot(data[:512])\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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jgx6E4/m+uocQveHmYgLVmtxXBzMsIr1bhpTZICFtALFUATVJ1jRZfG67Gn0jiHZIZUtY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\nHzQzsGgiovOVAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1209.6x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "YyjwHUwZfg9B",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Hyperparameters"
      ]
    },
    {
      "metadata": {
        "id": "-gp8Qw-6fg9C",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "RNN_CELLSIZE = 80 # size of the RNN cells\n",
        "SEQLEN = 32       # unrolled sequence length\n",
        "BATCHSIZE = 30    # mini-batch size\n",
        "DROPOUT = 0.3     # dropout regularization: probability of neurons being dropped. Should be between 0 and 0.5"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "WbsuSalyfg9E",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Visualize training sequences\n",
        "This is what the neural network will see during training."
      ]
    },
    {
      "metadata": {
        "id": "kYk8zx6jfg9F",
        "colab_type": "code",
        "outputId": "8f8ff1f4-99a1-4258-e109-370e40ccefd9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 426
        }
      },
      "cell_type": "code",
      "source": [
        "# The function dumb_minibatch_sequencer splits the data into batches of sequences sequentially.\n",
        "for features, labels in dumb_minibatch_sequencer(data, BATCHSIZE, SEQLEN, nb_epochs=1):\n",
        "    break\n",
        "print(\"Features shape: \" + str(features.shape))\n",
        "print(\"Labels shape: \" + str(labels.shape))\n",
        "print(\"Excerpt from first batch:\")\n",
        "\n",
        "picture_this_7(features)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Features shape: (30, 32)\n",
            "Labels shape: (30, 32)\n",
            "Excerpt from first batch:\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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7HYm8tTCGkX3a8vh9XXCyt1Y6nhCikXy3PRGDjCabhRF92rJun5YdcWlm2aVU\nmL6GbqVXnwaxYPrNL009H5h+RlPPB6afsTHyVVbrOZRwGR83W1Q1+Wi1BY2Q7BfN1SS2xa5R1uuv\nbHNhpYKZd3dSOo64BWuNFY+MCaN/Vz8++fY422LTOHouh+cmdqdPZxl5EsLc5RRVs+dYBoG+zvTr\nIoWXqQtu7Uo7f1fizmZTWFKFm4ud0pGEmWvsrfTq2iAWTL+xpKnnA9PPaOr5wPQzNla+bYdT0emN\njOoXQrt27Roh2S+a8z00zwnjd2Dr4VQycsoY2TeQtr7SudMchLRxZf5LUUwbG0ZJeTVzFh8m+lCK\n0rGEEA207WjOldHkUR1lCYyZGBHZFoPBeK1jqRANMXDgQKKjowFuuJXeE088QU1NDQBxcXG0b2+6\nu2EIIW5v97Er544hEebZ7fqqFlkoV1TV8k30eexs1DwyOuz2LxAmQ6O2YtKIjnz856HY22pYtuks\n5ZXS5EsIc3U5r5wj54sI8HFmQNfWSscRd2hIhD8atRXb49JkvahosF9vpTd37txrW+lt27YNZ2fn\na1vpTZ48GXd3d9lKTwgzlltYyamkPMJDPPBxd1A6ToO0yKnXP+y6SFFZNVNGdZQpY2Yq0M+FCcPb\ns3zzWdbsusCMcZ2VjiSEqIfvd1xdm9xBRpPNiLODDf26+LI/PpML6UV0aOt2w+cZjUYu5ZZRXSsd\nssWtyVZ6QliGPcczMBphWC/zHk2GFjiinF1QwQ+7L+LuYsf4oaFKxxENcF9UCB6udqzdk0ReUaXS\ncYQQdZSVX87OI+n4uNkysLtsz2duRvYJBGBbbNp1/240GklMK2TphtM89d52/vj+TpZtTbvRIYQQ\nQlgQo9HIrqPpaNRWDOxm/rPIWlyh/OWG09TqDDx6T2fsbVvkgLnFsLPRMG1MGDU6Ayu2nFU6jhCi\njtbsuojeYGRkLy/UMppsdrp38MLT1Y59xzOoqtFxLqWAxesSeOLdbcz6ZC9rdl2kuKwadxdbElJK\n0V4qVjqyEEIIBSVnlpCWVUqfcB+cHGyUjtNgLapQPpWUx4H4TDoGujGkp/kP9wsY1rstQX4u7DyS\nTnKmXIQJYS7yiyvZHpuGr4cDEe1bKR1H1IPaSsWw3gGUV+l49J1oXv10Hz/tSaK8spahvfx587E+\nrHhnLC9M6gnA6p0XFE4shBBCSVf3Th4aYb57J/9aiymU9QYjC386BcBTD3SVtXAthNpKxaP3dMZo\nhKUbzigdRwhxh37cnYROb2DC8PYymmzGRvUNxMZaDSoVw3sH8Pcn+rLinTHMmtqLfl38sLFWE9HR\nG39POw7EXyIzt0zpyEIIIRSUcnUvAAAgAElEQVSgNxjZezwDJ3trenfyVjpOo2gxhfK2w6kkZ5Yw\nvHfATZuOCPMU0dGbHu29OHY+h+Pnc5SOI4S4jeKyarYcSsHD1Y7hvVvGXWVL5evhyNK/j2L522P4\n85QIIjv7Yq1RX/cclUrFiF7eGIxXptsLIYSwPCcv5FJQUs3gHm1+d54wVy2iUC6rrGX55rPY2aiZ\nMa6T0nFEI1Oprowqq1RX1qDrDbJViRCmbN0+LdU1eh4cGtpiTpaWzNnBBmvNrS8Xuoe40MbLkZ1H\n0qT5ohBCWJiyipprMz+HtoBu11e1iEJ51bbzlJTX8PCIDni42isdRzSBdv6tGBrhT3JmCXuOpSsd\nRwhxE+WVtWzcr8XVyYZR/QKVjiOaiZWVioeGtUenN/LTniSl4wghhGgm5ZW1/P1/MWgzixndL5BO\nQe5KR2o0Zl8oZ+SUsn6fFh93B+6Paqd0HNGEpo3thLXGiuWbz1Fdq1c6jhDiBjYeSKa8Ssf9Ue2w\ns5GdByzJ0F4BeLraseVQCsVl1UrHEUII0cQqqmp5e2EMF9KLuCsygGcf6o5K1XL6kph9obx43ZWp\nuE/cF36l4YhosbzdHLhvcAh5RZWs2ysjFkKYmqpqHWv3JuFob83dA4OVjiOambXGivFDQ6mu0bNh\nf7LScYQwGXqDkaoandIxhGhUVdU63ll0iHOphQzt5c+fHu7Z4popm3WhfORsNkfOZtMt1JN+XfyU\njiOawYS7OuDsYMPqnRdkxEIIExN9OJWS8hruGRSMg5210nGEAkb1DcTF0Yb1+7VUVNUqHUcIRRWU\nVLFq+3memreNSbM38ul3J8gvljX8wvxV1eiYs+QwZ5ILGNyjDS9N6tkid7gw20JZpzewaG0CVip4\n8oGuLWqYX9yck701k0d2oKJKx6rtiUrHEUL8rFan54ddF7GzUXPfYFkGY6nsbDXcNziE8spatsSk\nKh2nRdAbjOQXV2KQRpZmwWAwcvx8Du99Fcvjc7ayYvM5Sspr8HZ3YOvhVJ6at52lG05TVlGjdFQh\n6qWmVs+7X8Zy8mIe/bv68fLUCNRqsy0pb8lsF5BtPJDMpdwyxg0IIsjPRek4ohmNHRDM+v1aNh1I\n5p5BwbT2dFI6khAWb3tcOgUlVTwwpB0ujjZKxxEKuntgMGt2XeSnPRe5Z1CwLIu6Q7U6A8mZxWRk\nl5GRU0p6Thnp2aVk5pZRozPwyJgwJo/sqHRMcRNFpdXsiEsj+lAql/PLAQhu7cKY/kEMjfDH1kbD\nriNpfL3lHGt2XWTLoVQmDm/PPYNDsJWfEWEmanV65i2N5URiLn06+/LqtN5oWmiRDGZaKBeXVbMy\n+hyO9tZMHR2mdBzRzKw1VswY15kPlh/hq41neGNmH6UjCWHR9HoDa3ZeuLZGVVg2Jwcbxg0IYs2u\ni+w4ks7Y/kFKRzJZhaVVbI9NY9fRdDJyyjD+ZtDY1kZNgK8zAT7O9JclZoqqqKolr6iS/OKqn//8\n6uOSSlIvl6DTG7GxVnNXZABj+gfRsa3bdTMeR/QJZHBPfzbuT+b7HYks3XiG9fu1TBkVRoinzBgQ\npq1WZ+CfXx3h6LkceoV58/rM3rfdOtDcmV2hbDQa+e+aeMqrdDz5QBdcnWyVjiQUMKh7a9budePg\nycuc1uYTHuKhdCQhLNae45fILqhg7IAg3F3slI4jTMD9Ue1Yt0/Lmp0XGNWnbYudllcfBoORUxfz\n2ByTwqGEy+gNV4qrEF8HQgO9CPBxJsDbGX8fJzxd7Vtccxxzk5hWyMKfTnEutfCmz7GxVtPW14W7\nIgMY3isAJ4ebz6qxtVbz4LBQRvUL5IddF1i7V8tn35/A08WGbh2KaevjTICvM219nPF2c5DvvzAZ\nS9YlEHsmix4dvJj9aB+sNS1/JoTZFcrfbU/k4MnLhId4MG6AdFW1VCqVij/c34VX/7OPxesS+PCF\nKDmZCKEAg8HI6p2JqH/eR1cIADcXO0b0acvmgynsi89kaIS/0pEUV1x2ZWrulkOpXM67MjU3yM+F\nMf0CGdorgOzL6YSEhCicUlxVWFrFso1n2R6XBkDXdp609nLEw9UeD1c7PH/+28PVDkd76zr3ynGy\nt2bGuM7cPTCYlVvPsyMujZ1H0q97jo21Gn9vJ9r+XDj7ezvT1tcZX3cHufkkmlVZRQ1bD6fi6+HA\nm4/1sZglNWZVKB9KuMyKLefwcrPn9RmRLXpOvLi9sEB3onq0Ye+JS+w9nsHQXgFKRxLC4sQkXCY9\nu4y7IgPwcXdQOo4wIQ8ODSX6UCqrdyQS1aONxd7MLK+s5YsfT7L/RCY6vQEbjRXDewcwtn8QHQPd\npBlpM8jKLyctp4LAQMNtC8xanYGNB7Ss3HqeiiodQX4uPDW+K13beTZJNg9Xe56f2IPRPZ1xcPUh\nLauUtOwS0rOurFHPyC5Fe6n4utdo1Fa09nK8NvugrY8zQa1dCPBxbpKMQuw8mk6NzsCYfkHY2ZhV\n+dggZvM/Tc0q4aNvjmJjreavj/WllbNMuRYw4+7OxCRc5quNZ+jX1c+ifniFUJrRaOS77YmoVDBh\nuIwmi+v5ejgS1bMNu49msPVwKmFB7qitVGjUVqitVKjVP3+stsLRTtNiC8ZFaxPYfTSDAB8nxvQP\nuu3UXNH43v0ylpTLJXy+PpXOwR50bedJt1BPgtu4XrelzbFzOSxce4qMnDKc7K155sFujOkX2Cyj\nt2orFW28nGjj5UT/rr+sR9cbjGQXlJOe9UuDt/TsUjJySknLKr3uGNPGhDFJGr6JRmY0GtkSk4JG\nrWJEn7ZKx2lWZlFVlFbU8O6SWCqr9bw2vTchbVyVjiRMhI+7A/dHtWP1zgus3ZMkJwghmtGx8zlo\nLxUzqHtr/L1lJEP83oTh7dl9NIMFq+Nv+byu7Tx556l+LW7N24nEHLbHpRHSxpWPXoyS6bIKefah\n7vy08zSpudUcOZvNkbPZADjaaQgP8aRLOw9Oa/M5fDoLKxWMGxDEI2M6mUQHf7WVitaeTrT2dKLv\nr/7daDSSX1xF2s+jzmv3JrFiyzmC27jSp7OvYnlFy3MmuYD07DKierSxuN5QJl8o6/UGPlh+hMv5\n5Uy8qz2De7RROpIwMRPvas/22DRW77zAyL6B0kxIiGayYX8yAA/JaLK4iUBfF16eGsHFjCL0eiM6\nvQGD4crfer0RncFAdkEFp5LyWLrxDE/e31XpyI2mqkbHgtXxWFmp+NPDPaRIVlCnYHdsh7UhJCSE\n/OJKTl3M41RSPqcu5hF7JovYM1kAhId48PT4rgS3Nv0BGZVKhWcrezxb2RPR0ZvwEA9e+3QfH319\nlI9eGkJrL9k6UzSOzQdTABgzIEjRHEow+UJ56cYznEjMJbKzD9PGdFI6jjBBDnbWPDImjAWr41mx\n+SwvTOqpdCQhWrycggqOnsumY1s3Qv1bKR1HmLBhvQIYdoseElXVOl769x7W7dXSvb1XixkN+yb6\nPFn5FTw4NFR+RkyIh6s9Q3sFXOtrkltYSYI2Dyd7a3p38jHbJQDt/Fvx/MM9+OibY8z9Mpb5L0Zh\nb2vyl/nCxBWXVXPgZCb+3k50scAdZkz69ubOI2n8tCeJAB8nXnmkl8U2AhG3N7JPWwJ9ndkel0Zy\nZvHtXyBEI5k3bx6TJk1i8uTJnDx58rrHDh48yIQJE5g0aRILFixQKGHTiD6citEIY2SPXNFAdrYa\n/jLjyn6cH688Tn5xpdKRKKuoISEpj/X7tHz63Qn+teIImXlld/z6i+lFrN1zET8PR6aMliVBpszL\nzZ5hvQKI7OxrtkXyVcN6BXDv4BDSs0v55NvjGH+7MbcQdbQjLh2d3sCY/kFm//NRHyZ7qyk1u4LP\nfkrG0d6avz7WFwc7a6UjCROmVlvx+H1deOt/MSxam8DcZwZY5A+0aF6xsbGkpqayatUqkpKSmD17\nNqtWrbr2+Ny5c1m8eDE+Pj5MmzaN0aNHExoaqmDixqHTG9h6OBVHe2sG9WitdBzRAgS3duWJe8P5\n4sdTzP/6GHOeGXBdk6WmVKvTc/h0FtpLxaRcLiE5s4S8ot8X6wlJecx5egBtfV1ueTyd3sCn353A\nYITnJnaXJpOiWT1+bzjaS8UcOJnJD7suytIYUW8Gg5Eth1Kw/rlTvyUyyRHlgpIqFm9ORa838Nq0\n3rLOQtyRiI7e9Arz5uTFPOJ+btQhRFOKiYlhxIgRALRr147i4mLKyq6MOqWnp+Pq6oqfnx9WVlYM\nGTKEmJgYJeM2msMJWRSVVnNX7wApAkSjGTcwmH5dfDmVlMf3OxKb5WvqDUbmLonl/WVH+H7HBeLO\nZGMwGIgI8+ahYaHMmhrBZ68M4w/3d6GgpJo3/nuApIyiWx7zpz1JaDOLGdmnLd3bezXL/0OIqzRq\nK/4yozcernYs23SG4+dzlI4kzNSpi3lczitncI82OFtop36TvMJZuyeJ4nIdj98bTkSYt9JxhBl5\n/N5wjifmsmTdaSI6este26JJ5eXlER4efu1zd3d3cnNzcXJyIjc3F3d39+seS09PVyJmo9t08EoT\nL5l2LRqTSqXihUk9uZixm5XR5+jazpPwJl4T9+3W8xw7n0OP9l5MuKs9QX4uN+zqGujngp2NhgWr\nT/DmFwd5+8l+hAW6/+55mbllrIw+RytnWx6/N/x3jwvRHNyc7XhjZiSvLzjAv1Yc4aOXhuDr4ah0\nLGFmNsekADCmX5CSMRRV70J53rx5xMfHo1KpmD17Nt26dWu0UGMHBOFsU80DQ9o12jGFZWjr68Lo\nfoFsPpjClpgU7hkUonQkYUEaYz1Yeno6tbW1dXqNVqtt8Ne9U9mF1Zy8mEdoG0dqy3PRanPv6HXN\nmbE+JF/DNVbGqcP8+PQnLf/86jCvTQrF0a5x7un/Nt/plBK+3ZaKu7M1D0d54qguJT+nlPybDMC1\n94Zpd/nz9Y4M/vr5AZ66O5DQNr/MeDMYjSz4KZkanYFHBviQk5VBXcfyGvP7HBIi5z9L1jHQnWce\n7Mpn38fz3tI43v/TIJkBJO5YYUkVhxIuE+TnQliQm9JxFFOvn5jbrctrKF8PRyLat5I1pqJeHhkd\nxp5jGXwTfZ6hEf44Weh0EdH0vL29ycvLu/Z5Tk4OXl5eN3wsOzsbb+/bz5AJCKjbOiCtVtusF8S7\n1iUA8OCwToSE3Nl2fc2dsa4kX8M1ZsaQEMirsOGb6HOsO1zI7Ef7NPh64Lf5svLL+XrnOaw1Vvzt\nDwPuuCt1SAj4t/HjXyuO8H8bUnnzsb7XZr5FH0rlYmY5fcN9GT+yR50zm8P3uSFuNcBy8OBBPvro\nI9RqNVFRUTz33HMKJm05RvcL4kJ6EdGHUlmwOp6nH+hKfnEVecWV5BdXkV9USX5JFXlFlRSXVfPg\n0PYM7inbsArYFpuG3mC02CZeV9VrXuqt1uUJoTRXJ1smjehAaUUNK7eeVzqOaMEGDhxIdHQ0AKdP\nn8bb2xsnpysjTP7+/pSVlZGRkYFOp2PXrl0MHDhQybgNVl2rZ0dcGq5ONvTr6qd0HNGCPTyiA13a\neXAoIYtNP+/h2Viqa/W8tzSO8spann2oW523bhrQrTVvPtYXgDlLDhNz6jIFJVV8uT4BBzsNf3yo\nm0VfWN7IrwdY3n33Xd59993rHp87dy6ffvopK1eu5MCBA1y8eFGhpC3P0+O70rGtG7uPZjDlb5t5\n/sNdvL3wEJ9+d4Jvtp4n+lAqR8/lcDGjmM9/OElZZd1mNImWR28wEn04FVsbNUMj/JWOo6h6jSjf\nal2eEKbg3sEhRB9KZcN+LUMi/OnQ1nKnjYimExERQXh4OJMnT0alUvHWW2/xww8/4OzszMiRI3n7\n7beZNWsWAOPGjSM4OFjhxA1zID6T0opaJgxvj7VG1v+LpqO2UvHKI73404e7Wbwugc7B7gS3dm3w\ncY1GI5+viUebWczofoGM6BNYr+P07uTDW0/2Y87iw/xzWRxBvi6UV+l49qFueLjaNzhnS3OzARYn\nJ6frGh8C1xoftoQdAkyBtUbNG49GsnBtAjW1ejxd7fFoZYeHiz2erezwcLXHw9WOjQeSWbbpLN9t\nT5T19Rbu+PkccgoqGNU3EEd7y951qFEWK9zJujxTX3dXX6ae0dTzQdNlfHCgN5+tTeajFbHMmhiK\nWl2/O/ym/h6aej5o2evuXnnlles+DwsLu/ZxZGRkoy5LUdqWmBRUKhjdr37FhRB14eFqz0tTejJn\n8WH+/n8xjO4fyKi+gXi7OdT7mNGHUtkRl06ovytPPdC1Qfm6hXrxj6cG8PaiGLSZxYSHeDDagpve\n3EpjNz6szzUlmP75sinzPTzot43xdEAZteVlZJVDtwAr3JysWbc3ia4BajxcbrxszZLfw8Zi6hnX\nbD8NQNe2GpPN2lzXlfUqlG+1Lu9mTH3dXX2YekZTzwdNmzEkBM5f1rMtNo0TaXom3tWhzscw9ffQ\n1POBeWQUt5ecWczZlAIiwryle6poNn06+/L4veF8u+08q7Yl8v32RCLCfBjTL5DenXxQ12Fng8S0\nQv7vx1M4O1jzxsw+2FirG5yvU7A77/5xIOv3aZkyqiNWzbT3s7lraOPDul5Tgumfi0wh3xP32/Hh\n10fZebKUv8yI/N3jppDxVkw9H5h+xmMnz3MmtZRQf1eG9uuidJwbas73sF5z5261Lk8IU/L4veG0\ncrbl263nycyVdfRC1NeWmBQAxsqWUKKZjR8ayld/H80LD/cgNKAVR85mM/fLWJ54dxvfRJ8jt7Dy\ntscoq9Tx3ldx6A0GXpnWG2/3+o9K/1aofyv+PCVCbiDdQlM0PhSNL6pnGzq0bcX++EzOpRQoHUco\nIOZMAQajbP94Vb0K5V+vy5s7dy5vvfVWY+cSolE4Odjw9Piu1OgMLFgd3yjb9whhaSqrdew6moGn\nqx2RnXyUjiMskJ2thpF9A5n/4hA+eXkoYwcEUVGlY+XW8/zh3a385bN9fPTNUb7aeIaN+7XEnLrM\nhfRCCkqq0OkNLNuWTl5RJVNHhxHRUYqw5mZpjQ/NlUql4on7rowiLlqXINdMFkavNxBzpgB7Ww1R\nPS27iddV9V6j/Nt1eUKYqoHdWtOnsy+xZ7LYHpvGyL6yvlKIuth7PIPKah3jh4bWaaqrEE0hpI0r\nzz7UncfuCWffiUtsiUnhTHIBZ5JvPAJmpQKD8UoDrofrsQRHNJylNT40Z52DPRjQzY+DJy+zPz6T\nwT1kuyhLEXc2m+JyHWMHBGFvK3tuQyM18xLClKlUKp55sBunknJZsv40vTv54OZip3QsIcyC0Whk\n08EUrKxUjOrbVuk4Qlxjb6thVN8rDb5qdfor+8IWV5H/8x6xv94r1qCvZdbUCFlDrCBLanxo7h69\nO5zY01l8tfEM/br4Yq1p+Hp+YdqMRiPr9l5pkCVLrH4hhbKwCF5u9swc15kvfjzF/346dcMmFUKI\n37uQXoT2UjH9u/rJtjfCZFlr1Ph6ON50nbBWq8XJ4cZdfIUQ1/PzdOTugSGs3ZvE+n3JPDhMtupq\n6XYdzeBUUh6d2jo1ylZ8LYXMoRMWY+yAYMIC3dgfn0ns6Syl4whhFjYfTAGksYcQQliSSSM74GRv\nzXfbz1NcVq10HNGEisuqWbQ2AVsbNROHyFT7X5NCWVgMKysVzz/cA41axedr4qmoqvsejEJYkrKK\nGvaeuISvhwM92t96C0AhhBAth7ODDZNHdaS8Sse3287f9HkGg5EjZ7OZtzSWZZvOoDdIAzBzs2ht\nAqUVNUwb0+mm+2dbKimUhUUJ9HVhwvAO5BVXsXzTWaXjCGGyDAYjX0efo6ZWz5h+QbK2UwghLMy4\nAcH4eTqy+WAKl36zxWZ5ZS3r9ibxx/d38M6iQ8Scusz3Oy7w7peHqazWKZRY1NXRc9nsPpZB+4BW\n3DvYdPd3VooUysLiPDyiPf7eTmw8mCz7BApxA9W1ev614ggb9ifj5+HIqH7SKV4IISyNtcaKR+/u\njN5g5Mv1pwFIyyrhv2viefQf0Sxcm0BuUSV3RQbwz+cG0aODF3Fnsnn9s/3kFd1+f3OhrMpqHf9d\nHY+VlYo/PdwDtdwQ/x1p5iUsjrVGzfMTe/D6gv386+ujzH8hilbOtkrHEsIkFJZW8e6SWM6nFRIe\n4sEbMyNxliZIQghhkfp39SM8xIPDp7PIzi8hJesUAJ6t7Hl4RBCj+gbi6nTlGuqtP/Tjix9OEn0o\nlVmf7OVvT/Ql1L+VkvHFLXy95Rw5hZVMvKu9NPC6CRlRFhYpPMSDqaM6klNQwdwvD1Ndq1c6khCK\nS80q4ZVP9nI+rZChvfyZ83T/axdAQgghLI9KpeLxe8MBSMmqoGs7T96YGcmi2SOYeFeH684RGrUV\nz03ozuP3hlNYWsXrC/ZzKOGyUtHFLSSmFbJ+XxJ+no5MGtlR6TgmS0aUhcWaPKojmfnl7D6awb9X\nHuO1ab1lHaawWMfO5/D+sjgqqnQ8MiaMSSM6oFLJz4MQQli6Dm3deP/5QRTkZTMosvMtn6tSqRg/\nNBRfD0fmf3OUeUtjefzeLtwfFSLnFBOh0xv49LsTGIzw/MTu2FrLPtk3IyPKwmKpVCpeeLgH4SEe\nHIjPZPlmae4lLNPmmBTeWXSIWp2BVx7pxeSRHeWCRgghxDWdgz1o7WF3x8/v39WPfz47CDdnWxav\nS+DzNSfR6w1NmFDcqR93XyTlcgkj+7SlW6jsaHErUigLi2atUfPmY31o4+XI6p0XiD6UqnQkIZqN\n3mBk8boE/rs6Hid7a959ZiBDIvyVjiWEEKIFCA1oxYcvDCHIz4XNMSl8sOIIRqNsH6WkzNwyvt16\nnlbOttem1Iubk0JZWDxnBxv+/od+ODvY8N818Rw/n6N0JCGaxddbzvLTniQCfJyY/2IUnYLdlY4k\nhBCiBfFys+f95wcRHuLBwZOX2X8iU+lIFstoNLJgdTw1OgNPj++KkzTqvC0plIUAWns68dfH+2Cl\nUvHPZXGkZpUoHUmIJlVZrWPjgWTcnG354E9R+Ho4Kh1JCCFEC+RgZ82Lk3pio7Hif2tPUVZRo3Qk\nixR9KJWTF/Po09mXgd1aKx3HLEihLMTPOgd78NLknlRU6fjHokMUllYpHUmIJrP7aDoVVTrG9g/C\nyd5a6ThCCCFaMD9PRyaP6khRaTVfbZKeMM0pObOYuUsOs2B1PPa2ap55sJv0IblDUigL8StDIvyZ\nNiaMnMJK5i45TE2tNJ4QLY/RaGTDgWTUVipG9w9SOo4QQggLMH5oKIG+zmyJSeFMcr7ScVq8jJxS\nPlh+hBfm7+bw6Sw6Bbkz95mBeLnZKx3NbEihLMRvPDyiA8N7B5CYVsTy7enoDdJ4QrQsCUn5pGWV\nMrBba9xd7ryLqRBCCFFfV/ZZ7gHAgtXx1OpkMKIpZBdU8Mm3x3nug53sO3GJUH9X3n6yH+8/P4gO\nbd2UjmdWZB9lIX5DpVLx/MQe5BVVcvJiHovWnuKpB7rKNBXRYmw4oAXg7kHBCicRQghhSToFuzO2\nfxCbY1L4cfdFHh7RQelILUZBSRWrtp1n6+FUdHojbX2dmTYmjH5d/OQatp5kRFmIG7DWWPHGo33w\nc7dlw/5kftydpHQkIRpFbmElhxKyCGntSqcg6XIthBCiec24uzNuzras2naezLwypeO0COnZpfzp\nw11sOpiCVysHZk2N4D+zhtG/a2spkhtACmUhbsLJ3ppn7g3Gw9WOLzecZu/xDKUjCdFgWw6lYDAY\nuWdQsJw8hRBCNDsne2uefKArNToDn68+KXsrN1BeUSV//18MJeU1PHZPOP/9y3CG9gpAbSXn+IaS\nQlmIW2jlZM3bT/bHwU7Dv1ce59TFPKUjCVFvNbV6og+l4OxgTVSEv9JxhBBCWKhB3VvTK8ybExdy\n2X1MBiLqq6S8hr//7yB5RZXMGNeJB4eFolFLeddY5J0U4jaC/Fx487E+gJF3vzxM6mXZY1mYp/3x\nmRSX1TCyTyC21mql4wghhLBQKpWKPz7UHRtrNYvWJlBSLnsr11VVtY5/LD5EenYZ90e1Y8Lw9kpH\nanGkUBbiDnQL9eKlyRGUV+l4e2EMeUWVSkcSos42HtCiUsHYAUFKRxFCCGHhfNwdeGR0R0rKa1i6\n4bTSccyKTm/gvWVxnE8tZGgvfx6/N1yWUzUBKZSFuENDIvx57J7O5BVX8c6iQ5RX1iodSYg7lphW\nSGJaEZGdfPH1cFQ6jhBCCMF9Ue0Ibu3Cttg0TiXJ8rY7YTAY+eTb4xw7l0OvMG9enNQTK1mP3CSk\nUBaiDsYPDeWegcGkXC5h3tJY2QNQmI2NB5IBuEe2hBJCCGEiNGornp/YA5UKFnx/gqoandKRTJrR\naGTxugR2H8sgLNCN12dEyprkJiTvrBB1oFKp+MMDXenf1Y+TF/P48Osj8kvdQtXW1jJr1iymTJnC\ntGnTSE9P/91zwsPDmT59+rU/er1egaRQXFbN3uOXaOPlRPf2XopkEEIIIW6kQ1s37hvcjku55Sxa\nm6B0HJO2eucF1u3TEuDjzN//0A87W43SkVo0eXeFqCO1lYpZj/Tirf/FcPDkZTJy9vKX6b1p6+ui\ndDTRjDZs2ICLiwvz589n//79zJ8/n48//vi65zg5ObF8+XKFEv5i6+FUdHoDdw8MlulZQohmVVtb\ny+uvv05mZiZqtZr33nuPgICA654THh5ORETEtc+XLl2KWi0NBy3JzLs7cfJiLtGHUono6M2Abq2V\njmRyog+lsmzTWbzc7PnHU/1xdrBROlKLJyPKQtSDrbWaOU/3555BwaRllfLyJ3vZeSRN6ViiGcXE\nxDBy5EgABgwYwLFjxxROdGN6vYFNB1Owt1VzV2TA7V8ghBCN6OpNxZUrV/LMM88wf/783z3n6k3F\nq3+kSLY81ho1r07rjY21mk+/O0FuoTRN/bXNMSksWH0CF0cb/vFUfzxb2SsdySLIiLIQ9WStUfP0\n+G50aefJf1Yd598rj7/AYoAAACAASURBVJOQlM9T47tiZyM/WneioKQKOxs1DnbWSkeps7y8PNzd\n3QGwsrJCpVJRU1ODjc0vd3hramqYNWsWly5dYvTo0Tz22GO3PW56ejq1tXVrFKfVam/6WLy2mLyi\nSgZ1cScr8/fTw5vLrTKaAsnXcKae0dTzQeNmDAkJabRjNURMTAwPPPAAcOWm4uzZsxVOJExVgI8z\nT97fhQWr4/lo5VHmPjMQtcyC4vsdiSzbdBZXJxveebI//t7OSkeyGHI1L0QDDezWmpDWrry/PI5t\nsWmcTyvk9RmRBPjIL7JbSUwrZPbnB+gb7sur03orHeeWvv/+e77//vvr/i0+Pv66z41G4+9e99pr\nr3HfffehUqmYNm0avXv3pmvXrrf8Wr+dkng7Wq32lhfEi6MPADBlbHfFlgfcLqPSJF/DmXpGU88H\n5pGxPpripmJ9biiC6d8sMfV80PQZ23sb6RbiwsmkfP63+jCje3vX6fUt6T00Go2sj8lix/E8WjlZ\n8+x9QahqCtBqC0win5Ka66ZivQvl2NhYXnzxRebNm8ewYcPqexghWgQ/T0f+9afBLFl3mg0Hknn5\n4z08O6E7w3rJVNcbuZxXzj8WH6K2Vm8W79HEiROZOHHidf/2+uuvk5ubS1hYGLW1tRiNxusu/ACm\nTJly7eN+/fqRmJh420K5MaVmlXDyYh7d23vKGnohRJNrrpuKdb2hCKZ/I8LU80HzZXz9sQD+9OEu\ntsTlMOz/2bvvuCrr/o/jrzPYeyOKLEUQRMUtuc1VWVo5Sm1YqWVlZdntXdmvsmndDbsrTRtqd+6y\nstRyK+JGwIGIILIRBJF9zvn9YZGWqczrOvB5Ph488BzOePMVvlyf6/qO7sGE+Lve0POaUhsajCY+\nWR3Lb4fyaOlhxytTeuPpYquafEpqzIy1mqN85swZvvjiiysWXhCiubPQ65gyOoJZk7qi0Wh475uD\n/HdV7FUPCpqzwuJyXl4YTWFxBVNHR9A11EvpSLUSFRXFL7/8AsCWLVvo0aPHFV9PTk7mmWeewWQy\nUVVVxcGDB2nbtm2jZvxjS6hbotT9R08I0TTcfffdrFix4oqPUaNGkZubC3DNk4p2dnbY2tpWn1QU\nzZeDrSXP3NMFk8nEO8sOcLG05qMHzFlllZF5S/ezYU8qgS2dePOxPo1SJIu/q1Wh7OHhwfz583Fw\nkKGlQvzVTR1b8v7T/QjwceTn6BRW/CZ/8P9QVlHFq4tjyMi7yF0D2zK8t/nu6TtixAiMRiPjx49n\n2bJlPPPMMwAsWLCAQ4cOERgYiLe3N3fddRfjx4+nX79+RERENFq+0vIqth44i7uTNd3bm+fJCCGE\n+TOHk4pCfTq0cefuQcHk5Jfw39XN56JDWUUVr30Rw87YDMIC3Xh9WhTODlZKx2q2ajX02sam5iut\n1fcCNWqh9oxqzwfqz1jbfJOHtmTeylKW/XwcW20pYf4NM/RV7e0HlzIajSYW/3KGE6lFdAl2JirE\nqlbZ1TIk6I9tTv7qkUceqf73s88+25iRrrDjcDql5VWM6heETicbHAghlDFixAh2797N+PHjsbS0\n5M033wQunVTs1q0bnTt3rj6pqNVqGThwYKOeVBTqNX5IO2JP5rL9UDpdQjwZ2LW10pEaVHFpJa98\nvodjKfl0DfVi1qSusjiswq7b+lebb/L444/Tp0+fGr1RfS9QowZqz6j2fKD+jHXNN8fJk1nzd7D0\nt3Tem9GGlh729ZhO/e0HlzIGBASwYG0ccaeLiGjjzr8n98JCL8VbQ/olOgWtBm7u4ad0FCFEM6b2\nk4pCvfQ6LTPv7cIT727l0zVHCPF3xce9fo+j1CLr3EVe/3IvpzOK6NupJTPGR8pxkgpct1C+2iI2\nQogb08bXmcfu7sR//neQuV/EMO+Jvma5FVJdrd16ih93naa1twP/ur+7dP4N7NTZ85xMO0/39t6y\n16IQQgiz5e1mx6N3deTdZQd45fM9DO8dQLdQL3zq+cKDkrYdPMvHq2IpLa9iRG9/HhkVIdtiqYRc\nzxeigQ3s6sup9POs257Mf/53kH/d1x1tM+oAD548z1cb03B1tOblh3phb9P8ThQ0tg17UgEY2kuu\nJgshhDBv/SNbcSIlnx93nebz7+P5/Pt4Wrjb0S3Ui66hXoQHuWGh1ykds8bKyqtY8F0cm/aewdpS\nx1PjIxnYVf07gTQntSqUt27dyqJFi0hOTiYhIYElS5awePHi+s4mRJPx4K1hpGQUsSc+i+W/JjJ+\nSDulIzWKuFN5LP31LDZWel5+uCceLnJ1s6GVllex9eClRby6tKvZ/pNCCCGEGk0ZHcFdg9qy/1gO\nB45nczgxh3U7klm3IxlrSx0d23rg46Ih84I1TvaWONlb4eJghZ2NBRqN+i5OJKcX8vaS/aTnFhPU\nyonnJnRtUlfJm4paFcr9+/enf//+9RxFiKZLp9Py3MSuPP3+Nr7ZcJxAH0d6hLdQOlaDOnQih7e+\n3ocJE7Pv70aAj5PSkZoFWcRLCCFEU+TmZMPQnn4M7elHZZWBo8n57DuWzf5jWcQkZF160M7MK56j\n12lwtLPC2d4KT1cbBnTxpUeYt2J/H00mEz/uTGbRugSqDEZu7xvEfbeEmuUV8eZAhl4L0Uic7K2Y\nfX93npu/k3e/Oci7T/bF16vpbbFmMpn44fc/AlqNhomDfekULFc2G4ss4iWEEKKps9Dr6BjsQcdg\nDx66PZyMvGJ2H0jEytaZ88XlFBaXc/7C75+Ly8k8V0xyRiF74rNwdbRmWE8/hvT0w82p8Ua6FV2s\nYNHPZ4g7XYSjnSVPjY+ka6hs36hmUigL0YiCWjnzxJhOzFt2gLlf7OXdJ/ti14Tm7FZWGflkdSyb\n9p7B2cGK2fd1x8p0XulYzYYs4iWEEKI58nG3J7Kt8zV3AknNKuKX3SlsPpDGNxtP8O2vifQM92ZE\nrwAi2ro36BDtnIISZs3fSd75UiLauPP0PZGNWqSL2pFCWYhG1i+yFafSC1m7NYl5yw40mX3yzl8o\n542v9nL0dD6BLZ144YEeeLjYkJwshXJjkUW8hBBCiKvz83ZkyugIJt3Snm0Hz7J+92l2H8lk95FM\nWnrYMSIqgFt6B9T7sGyDwci8pQfIO1/K0K6eTBvbU1a1NhPmf3QuhBm6b0QoKRmF7D+WzdPvb2Pm\nvV0JbGm+c3hPZxTy6uIYcgtKierow4yxnbG2ku6lMckiXkIIIcT12VjpGdbLn6E9/TiRWsD63afZ\ncTiDhd/FcyKlgKfviazXYnn5r4kcS8knqqMPw7u7SpFsRmSlFyEUoNNpeeHBHozsE0hadjHPfLCN\ntVuTMBpNSkersei4DJ77aAe5BaXcOyyEWRO7SpGsgD8W8RrSw08W8RJCCCGuQ6PREOLvytP3dOHL\nl4YQ6u/K9sPpvL/8EIZ6Oh6LP5XH8k0n8HSxYfrdnVS5Arf4Z3I0K4RCLC10PHxHByJDPHn/20Ms\n/iGBg8dzmDG+syrmrRQWl7N6SxIlZZVY6LVY6HXodRos9Lrfb2vJKShh3fZkrCx1/Ou+bvSO8FE6\ndrMli3gJIYQQteNkb8XLD/dkzoJoth44i1aj4cmxndHW4ervhZIK3l12ADQaZt7bFXsbC3LqMbNo\neFIoC6GwLiFefPTMAD5ccYh9R7N5fN4WHh/TmV4dlNs+6mzOBV75PIbMcxev+1hPFxteeLCHbP+k\nIFnESwghhKgbW2sLXn64Fy8t2M3m/WnotBqm392pVsWyyWTioxWHySss495hIYQGuDZAYtHQpFAW\nQgWcHax48cEerN+dwuJ18bz+5V6G9vTjoZHhjT6MOS4pj9e/3EtxaSV3D2rLgC6+VBmMVFZd+qiq\nMlJpMFJZZcBogo5t3LG3tWzUjOJKsoiXEEIIUXd2Nhb83yO9efHTXWzaewatVsOjd3ascbH8y55U\nouMyCQt04+5BwQ2UVjQ0KZSFUAmNRsMtUQGEB7kxb+kBNuxJ5XBiLjd19KFLqBeh/q7oG3ju6a97\nU5m/MhaNBmaM68ygbq0b9P1E3ZVXGGQRLyGEEKKe2NtY8MqU3rzw6W427ElFq9UwbXTEDc8vTs0q\n4vPv4nCwtWDmvV1k8S4zJoWyECrj5+3IezP68vX6Y/y48zSrtySxeksSNlZ6OgV7ENnOk8gQTzxd\nbOvtPY1GE0t/OcbK305ib2PB7Ae60yHIvd5eXzScg0mFlJZXMapfkCziJYQQQtQDB1tLXp3Sm39/\nsoufd6eg02p45I4O1y2WyysNzFt6gIoqIzMndJXpUGZOCmUhVMhCr2PyyHDuHRpC3Kk8Dh7P4cCJ\nHKLjMomOywTA18sBPw8LfE6UY2mhxcpCh6WFDku97vd/a7Gx1tPayxFnB6t/fK/ySgP/+d9BdsVm\n0MLdjjkP9aSlh31jfauijnYn5MsiXkIIIUQ9c7Sz5LWpl4rlH3eeRqvVMGlEe6wsdP/4nC9+SCAl\ns4jhvf0VXWtG1A8plIVQMWsrPd3ae9OtvTcAGXnFl4rm4znEncojLdsA8fnXfR1XR2sCWzoR1NKJ\nwN8/vFxtOV9cztzFezlxpoCwQDdm398dRzuZb2wuTp09z5mcUlnESwghhGgATvZWvDY1itmf7GLd\n9mR+3JFMC3d7AnwcCfBxwt/HkYAWTrg7WxOTkMVPu07T2tuBySPDlY4u6oEUykKYER93e3xusufW\nmwKpqDQQffA47h7elFcaqLjso7zSSEWlgQslFaRkFpGcXsj+Y9nsP5Zd/Vp2NhbotBqKLlYwoEsr\nHh/TCQv9P58lFeqzMUYW8RJCCCEakrODFXOn9Wb15iSSzp4nJaOQnbHF7IzNqH6MnY0FBoMRS72W\n5yZ0veZVZ2E+pFAWwkxZWujw9bAhMNDthh5fWFzOqfRCkqs/zpNfVMaEYSGMGRx8w4tUCPXQaDT4\netrIIl5CCCFEA3JxsOah2y9dJTaZTOQWlJKSWcTpjEJOZxaRklFI1rkSpt3ZEb8WjgqnFfVFCmUh\nmgkne6tLC4FdVlSZTCYpkM3Y1NERJCfbyyJeQgghRCPRaDR4utri6WpL9zDv6vvlmKrpkaMrIZox\n6dCFEEIIIepOjqmaHimUhRBCCCGEEEKIy0ihLIQQQgghhBBCXEYKZSGEEEIIIYQQ4jJSKAshhBBC\nCCGEEJfRmEwmk9IhhBBCCCGEEEIItZArykIIIYQQQgghxGWkUBZCCCGEEEIIIS4jhbIQQgghhBBC\nCHEZKZSFEEIIIYQQQojLSKEshBBCCCGEEEJcRgplIYQQQgghhBDiMnqlA1zN66+/TmxsLBqNhtmz\nZxMREaF0pGoxMTE8+eSTtG3bFoDg4GBefPFFhVNdkpiYyKOPPsr999/PhAkTyMzM5LnnnsNgMODh\n4cE777yDpaWlqjI+//zzJCQk4OzsDMDkyZPp37+/YvnefvttDhw4QFVVFVOmTKFDhw6qasO/5tu8\nebOq2q+0tJTnn3+ec+fOUV5ezqOPPkpISIiq2rApkb6ydtTeV6q9nwTpK+tC+snGJ31l7UhfWXfS\nV9aeKvpKk8rExMSYHnnkEZPJZDIlJSWZxowZo3CiK+3Zs8f0+OOPKx3jby5evGiaMGGC6YUXXjAt\nWbLEZDKZTM8//7xp/fr1JpPJZHr33XdNy5YtUzLiVTPOmjXLtHnzZkVz/SE6Otr00EMPmUwmkyk/\nP9/Ur18/VbXh1fKpqf1MJpPpp59+Mi1YsMBkMplMZ8+eNQ0ZMkRVbdiUSF9ZO2rvK9XeT5pM0lfW\nlfSTjUv6ytqRvrLupK+sGzX0laobeh0dHc3gwYMBCAoKorCwkOLiYoVTqZ+lpSULFy7E09Oz+r6Y\nmBgGDRoEwIABA4iOjlYqHnD1jGrSrVs3PvjgAwAcHR0pLS1VVRteLZ/BYFAsz9WMGDGChx9+GIDM\nzEy8vLxU1YZNifSVtaP2vlLt/SRIX1lX0k82Lukra0f6yrqTvrJu1NBXqq5QzsvLw8XFpfq2q6sr\nubm5Cib6u6SkJKZOncr48ePZtWuX0nEA0Ov1WFtbX3FfaWlp9XAENzc3xdvxahkBli5dyqRJk3jq\nqafIz89XINklOp0OW1tbAFatWkXfvn1V1YZXy6fT6VTTfpcbN24cM2fOZPbs2apqw6ZE+sraUXtf\nqfZ+EqSvrC/STzYO6StrR/rKupO+sn4o2Veqco7y5Uwmk9IRruDv78/06dMZPnw4aWlpTJo0iY0b\nN6p+LpHa2vEPt99+O87OzoSGhrJgwQLmz5/PSy+9pGimX3/9lVWrVrF48WKGDBlSfb9a2vDyfPHx\n8aprP4Bvv/2WY8eO8eyzz17Rbmppw6ZIbW0rfWX9UWM/CdJX1pX0k8pQW/tKX1l/pK+sHekr/5nq\nrih7enqSl5dXfTsnJwcPDw8FE13Jy8uLESNGoNFoaN26Ne7u7mRnZysd66psbW0pKysDIDs7W5XD\nU3r16kVoaCgAAwcOJDExUdE8O3bs4NNPP2XhwoU4ODiorg3/mk9t7RcfH09mZiYAoaGhGAwG7Ozs\nVNWGTYX0lfVHbb/nf6W233OQvrIupJ9sXNJX1h+1/Z7/lZp+z/8gfWXtqaGvVF2hHBUVxYYNGwBI\nSEjA09MTe3t7hVP9ad26dSxatAiA3Nxczp07h5eXl8Kprq53797Vbblx40b69OmjcKK/e/zxx0lL\nSwMuzX35Y9VHJVy4cIG3336bzz77rHq1PzW14dXyqan9APbv38/ixYuBS8PdSkpKVNWGTYn0lfVH\n7T+javs9l76ybqSfbFzSV9Yftf+cqun3HKSvrCs19JUak1qu+19m3rx57N+/H41Gw5w5cwgJCVE6\nUrXi4mJmzpxJUVERlZWVTJ8+nX79+ikdi/j4eN566y3S09PR6/V4eXkxb948nn/+ecrLy/Hx8eGN\nN97AwsJCVRknTJjAggULsLGxwdbWljfeeAM3NzdF8i1fvpyPPvqIgICA6vvefPNNXnjhBVW04dXy\njR49mqVLl6qi/QDKysr497//TWZmJmVlZUyfPp3w8HBmzZqlijZsaqSvrDm195Vq7ydB+sq6kn6y\n8UlfWXPSV9ad9JV1o4a+UpWFshBCCCGEEEIIoRTVDb0WQgghhBBCCCGUJIWyEEIIIYQQQghxGSmU\nhRBCCCGEEEKIy0ihLIQQQgghhBBCXEYKZSGEEEIIIYQQ4jJSKAshhBBCCCGEEJeRQlkIIYQQQggh\nhLiMFMpCCCGEEEIIIcRlpFAWQgghhBBCCCEuI4WyEEIIIYQQQghxGSmUhRBCCCGEEEKIy0ihLIQQ\nQgghhBBCXEYKZSGEEEIIIYQQ4jJSKAshhBBCCCGEEJdRbaGclpamdITrUntGtecD9WeUfHVnDhnN\nmTm0r9ozSr66U3tGtecD88hoztTevmrPB+rPqPZ8oP6Mas8HjZtRtYVyZWWl0hGuS+0Z1Z4P1J9R\n8tWdOWQ0Z+bQvmrPKPnqTu0Z1Z4PzCOjOVN7+6o9H6g/o9rzgfozqj0fNG5G1RbKQgghhBBCCCGE\nEqRQFkIIIYQQQgghLiOFshBCCCGEEEIIcRm90gEaQk5+Cet3n0av0+Job4mjnRVOdpY42lniZG+F\no50llhY6pWMKIUSzYjSaKLhQRm5BKTkFJWTnl5BfWIbRZEKr1aDVatBptWg1VN+20Gvp3cEHXy8H\npeMLIUSTVFZRxY87T9O9vRetvR2VjiOEajS5Qrm4tJKXFuwmPffiNR9nZ2NBeKAbXUO96BLihYeL\nTSMlFEI0J2+//TYHDhygqqqKKVOmMGTIEKUjNZrk9EJ+jk4hOS2XC2XJ5BaUUmUw1vh1vt2YyJhB\nbblrUFss9HKSUwgh6kt+URmvLY7hZNp5NsWkMv/ZAdLPCvG7JlUoG4wm3lm6n/Tci9zWJ5BeHVpQ\nVFxB0cVyCi9WUFhcTtHFCoqKK8jOLyEmIYuYhCwA/Fs40jXUi66hXoT4uaDTyah0IUTd7Nmzh5Mn\nT7J8+XIKCgoYNWpUsyiUs85dZOnPx9l26Gz1fc72VgT4OOLpYouHiw1errZ4utji7myDTqvBaDJh\nMJowGk2X/m249DnvfClf/XSUbzaeYEdsBk+M6USIv6uC350Q4noSExN59NFHuf/++5kwYcIVX9u9\nezfvvfceOp2Ovn378thjjwHw+uuvExsbi0ajYfbs2URERCgRvVk5nVHIK4tiyDtfiqerLRl5F1m7\n9RRjBgcrHU0IVWhShfJXPx3l4PEcuoZ6MXlkODqt5pqPz8y7yP5j2ew/nk1cUh4pmUWs2nwSOxsL\nOgd7EB7oRjt/V/xbOKKXwlkIUUPdunWrPthzdHSktLQUg8GATtc0z9YXXChjxaZEftmTQpXBRGBL\nJyYOD8Vee4GQdm1q/bo9wrz56qej/BydwnPzdzCidwCTRoRia21Rf+GFEPWipKSEV199lV69el31\n66+99hqLFi3Cy8uLCRMmMHToUPLz80lNTWX58uWcOnWK2bNns3z58kZObr5Kyir5eXcKHdt60MbX\n+Yaes+9oFu8s3U9puYFJI0IZ0TuAqW/9xvJfE+kX2QovV9sGTi2E+jWZQnnz/jOs3ZpEK097Zt7b\n5bpFMkALdztu6xPIbX0CKauo4khS3qXC+Vg2O2Mz2BmbAYCVpY42rZwJ8XOhnZ8rIf4uuDhYN/S3\nJIQwczqdDlvbSwcbq1atom/fvk2ySC4pq2TN1iS+33aKsgoDLdzsmDA8hJs6tkSr1ZCcfO2pMNdj\na23BtDs70i+yFfNXHuanXaeJic9k2l0d6d7eu56+CyFEfbC0tGThwoUsXLjwb19LS0vDycmJFi1a\nANCvXz+io6PJz89n8ODBAAQFBVFYWEhxcTH29vaNmt1cLVqXwMaYVAA6B3tw96BgwoPc0Gj+fixs\nMpn4YWcyi76PR6/T8vykbkR19AHgwdvCeO+bg3z+fRz/fqBHo34PQqhRkyiUT6TmM39lLHY2Frzw\nYA/sbGp+lcHaUk/39t50b++NyWQiPbeY4yn5HE8t4ERqAUdPnyMh+Vz1471cbbkpzAl//wC0N1CU\nCyGar19//ZVVq1axePHi6z42LS2NysrKGr1+cnJybaPVicFoYkfcOTbuz+FimQEHGz239fOhV6gr\nOl0FKSmn6zWjNTBjlB+bDuSy6UAury6KoXMbJ27p4YWHs1WdXlupNrxRas8H6s+o9nxQvxkDAwPr\n7bVqQq/Xo9df/fAyNzcXV9c/p064urqSlpZGQUEBYWFhV9yfm5srhfINiE3MZWNMKr5eDrg4WHEo\nMZdDibm083Ph7oFt6dbeu/o41WAwsuC7ONbvTsHZwYoXH+xBcGuX6tfqH9mKDXtS2ROfxf5j2XQN\n9VLq2xJCFcy+UD5XWMrrX+7FYDDy3IM9aOlR905Vo9HQytOBVp4ODO7uB1y6YpJ45lLRfDy1gITk\nc6zekcmJjF08ObYz3m52dX5fIUTTs2PHDj799FM+//xzHByuv3Kzr69vjV4/OTlZsQPiT1bHsn53\nJrbWeiYMD+H2PkFYW/39z0p9Zwxu24Zb+xcxf8VhDiUVcCipEF8vB3qGe9M9zJtgX5cancBUsg1v\nhNrzgfozqj0fmEfGxmIyma759dqcUAT1nyypab7ySiP/+TYRrQbG9vXC19OGlI5O/Howl7jTBbz2\nxV68Xa0YHOlBez8Hvt6UxvEzxbRws+aRW/zQVxWQnFxwxWve2t2VY6fP8fGKgzw/vi0W+iunHja1\nNlSC2jOqPR803klFsy6UyysNzP1iL/lF5UweGU5kO88Gey9baws6BXvSKfjSexQUlfHO19HEnTrH\n4/O2cP8t7RneW64uCyH+dOHCBd5++22+/PJLnJ1vbN6YuYiOy2D97hT8vB2YOy0KJ/u6XdGtKT9v\nR96a3odth86y83AGhxNzWPnbSVb+dhJnByu6t/emR5g3HYM9sJLtAIVQBU9PT/Ly8qpvZ2dn4+np\niYWFxRX35+Tk4OHh8Y+vU9MTiqD+ExG1yff59/GcK6rkzgFt6Nfz0hX5wEAY2BtSs4pYvfkk2w6l\ns/TXs+i0GgxGE11DvXh2Qpd/XOMhMBBOZBr5btspDqYYGD/kz/UlmmIbNja1Z1R7PmjcjKoslA1G\nE+eLKzEaTf9YeJpMJuavOMzJtPMM7OrL7X0b9z/VxdGaycNbk3beks/WxvHp2jh2x2XyxNjOsgCC\nEAKA9evXU1BQwIwZM6rve+utt/Dx8VEwVd3lFpTy4fLDWFroeG5i10Yvkv+g1WoY0MWXAV18Kauo\nIjYxl5iELPYezWJjTCobY1KxtNBxe99AJg4Pvep8PSFE42nVqhXFxcWcPXsWb29vtmzZwrx58ygo\nKOCjjz5i3LhxJCQk4OnpKcOur+N4aj7rdpzCx92O8UND/vZ1P29Hnr6nC/cOC2Xt1iQ27z/DzT38\nePDWsOvu7DJ+SDu2H0pn1W+JDOjSSkZNimZLlYXy/zYcZ/mvidhYncTP2xF/Hyf8vR3w93HCr4Uj\n9jYWrN2axNaDZ2nn58Jjd3VU5ABIo9HQv4svEW09+HhlLHuPZvH4vM08cGsYw3r5y0GZEM3c2LFj\nGTt2rNIx6pXBYOTdbw5QXFrJY3d1pLW3o9KRgEvrTPQIb0GP8BYYjCYSUwuISchk26F0Vv52koul\nlUwZFSGjfoRoYPHx8bz11lukp6ej1+vZsGEDAwcOpFWrVtx88828/PLLPPPMMwCMGDGCgIAAAgIC\nCAsLY9y4cWg0GubMmaPwd6FulVUGPlx+GJMJnhjb+ZqjZrxcbZk6OoIpozrc8HGprbUFk0eG8c7S\nAyz4Lo6XJvesr+hCmBVVFspRHX1ISs0m74KRk2nnOZ565fwJDxcb8s6X4uZkzez7u2Op8LA6V0dr\nXniwO1sOnGXBd3H8d/URdh3JYFT/Nvi3cMTV0VqKZiFEk7D810QSks8RFeHD0J5+Sse5Kp1WQ2iA\nK6EBrozq34YXAurKLAAAIABJREFUPt3N+t0pGE0wbbQUy0I0pPDwcJYsWfKPX+/WrdtVt36aOXNm\nQ8ZqUlb8epK07AuM6O1PWKDbDT2npsehfTq1ZMOeVPYdzSYmPpMe4S1qE1UIs6bKQjnAx4lJQ1oT\nGBhIZZWBsznFnM4oIjWziJTMIlIyC7G10vPvB7rj6qiObZo0Gg0Du/rSsa07H6+KZd/RbGJPXppv\nY2etp7W3I629HfDzdsSvhQOtvRxxdlBmuKIQQtRG/Kk8lm86gYeLDdPvVmYkT0052Vvx2tTevPRZ\nNL9Ep2AwGJl+dycploUQZul0RiErf0vE3dmG+25p32Dvo9FomDo6gsfnbWHBd3F0DP7nOeNCNFWq\nLJQvZ6HXEeDjRICP0xX3m0wmVR6kuTnZ8OKDPTh0IpcTqfmkZl0gNauIE2cKOJaSf8Vje3VowTP3\ndpGFZoQQqld0sYJ3lx0AjYZn7+2Kva2l0pFumJO9Fa9N682Ln+1m094zGE0mHh/TGZ0Uy0IIM2Iw\nGPlwxWEMRhOP3dXxHxfkqi++Xg7c0S+I1VuSWLX5JL2D5QKPaF5UXyj/EzUWyX/QaDREhngSGfLn\nKtx/XBlPzbrAmawiDp3IITouk5cXRvPigz0avLMTQojaMplMfLTiEHmFZUwYFkJogOv1n6QyDraW\nvDalNy8tiOa3fWkYjCZmjIuUYlkIYTa+355MUtp5BnRp1Wh7HI+9uR3bDp5l9eYk/N2CUPmCyELU\nK7MtlM3NX6+Mjx/SjnnLDrD7SCb//mQXLz/cS7GVY4UQ4lrW705hT3wWHYLcuWtQsNJxas3e1pJX\np/RmzsJoth44i9Fo4unxkUrHEkKI68rIK2bZL8dwtrfiods7NNr72ljpeej2Drz59T7e+vYkn/yQ\niq+XA629HP787O2Ai4MVGo2Gsooq8gvLOFdYRl5hKecKyzh3vpRzRWW0a+3CnQPbNlp2IepKCmWF\nWOh1PDehKx+vimXT3jM8//FOXp3SG3dnG6WjCSFEtZTMIhati8fB1pJn7jX/K7B2Nha88kgvXl64\nh+2H0jEYTdzZy/yukAshmg+j0cRHKw5TUWXkqdEdcLRr3KkvvSNaMO3OCHYcOM25YiPHTp8jIfnc\nFY+xs7FAAxSXVv7j60THZeLiaM3ArjXfB1sIJUihrCCdTsvjYzphZ2PBd9tOMWv+Dl6d0hsfD9k7\nUAihvLKKKt5eso/KKiPPT+qMm1PTOJFna23Byw/35JVFMeyKzeBicTEvBwWa/UkAIUTTtHl/GvGn\nztEz3JuoCJ9Gf3+NRsOI3gGEeJsIDAykvNJARm4xZ7IukJZ9gTPZlz5rNNDG1xl3JxvcnKxxc7bB\n3ckaNycbjEYTL3y6i49XxRLg4/i3tYeEUCMplBWm0Wh48LYw7G0tWPrzcWZ9vJNXHuklHYgQQlEn\n0wqYvzKWtOxibr0pgO5h3kpHqle21ha8/FBPXv58D4dPnePjlYd5fEwnVa9/IYRofioqDSzbcBxL\nvZYpoyJU0UdZWVx9od3reWp8JK99sZfXv9zLf2b0M6tFIUXzpFU6gLhULI8d3I6poyM4f6Gcf328\nk6Onz13/iUIIUc9KyipZ8F0cMz/YTnJ6IYO6+fLArWFKx2oQ1lZ6Xprcg1Ye1mzae4YvfjyKyWRS\nOpYQQlRbvzuFvPOl3HpToNlPz+sR3oIxg4PJOlfCe/87iNEo/a1QNymUVeSWqACeuSeS0goDL34W\nzaETOUpHEkI0EyaTid1HMpj21mZ+2JFMC3c75k7rzYxxkVg24S3sbK0tmHZbAC097Fm79dIWKEII\noQYlZZWs/C0RW2t9k1kE656hIXQK9mDf0WxW/paodBwhrkkKZZXp38WXfz/QHUwm3l6yn/MXypWO\nJIRo4nLyS3ht8V7e+GofRRcruGdoCB/NHEBEGw+lozUKext99WKKX68/xs/RKUpHEkIIvt92iqKL\nFYzu36bRF/BqKDqthpn3dsHDxYZlG45zUC4KCRWTQlmFurf35r5b21NcWsmidfFKxxFCNFEGg5G1\nW5N49J3N7D2aRUQbdz6a2Z/xQ9phoW+6V5GvxsPFhlen9MLJ3pJPVsey41C60pGEEM1YYXE5a7cl\n4Wxvxci+QUrHqVdO9lY8P6kbOq2WeUv3k51fonQkIa5KCmWVuiUqkLa+zmw9eFaGYAshGsSHKw6z\n+IcErCx0PDW+M69N7U0rTwelYymmlacDLz/cC2tLPe/97wAHjmcrHUkI0Uyt/O0kpeUGxgwOxsaq\n6a29G9zahamjO3ChpJI3v9pLRaVB6UhC/I0Uyiql02qYfncntFoN/10dS1lFldKRhBBNSEx8Jpv3\np9GmlROfzBrEwK6tVbGaqtLatHLmxck90Go0vP7lPllYUQjR6HIKSli/+zSeLjYM6+WndJwGM6SH\nHzd3b03S2UI+WxundBwh/qZOhXJiYiKDBw9m6dKl9ZVHXCawpRO39w0i61wJyzfJggdCiPpxoaSC\nj1fFotdpeWp8ZJOZ+1ZfOgS5M+u+blQZjLzy+R5OZxQqHUkI0Yx8u/EElVVG7h0W0qSnwWg0GqaM\njiColRMbY1LZGJOqdCQhrlDrQrmkpIRXX32VXr161Wce8Rf3DGmHp4sNa7cmkZJZpHQcIUQTsGBt\nHAUXyrl3WAitvR2VjqNK3dt789S4zlwsq+LlhdEUFJUpHUkI0QykZV/gt31naO3tQL9IX6XjNDgr\nCx3PT+qGvY0Fn6yOlSkvQlVqXShbWlqycOFCPD096zOP+AtrKz3T7uyIwWhi/srDsuecEKJOouMy\n2XrwLMGtnRnVr2ktEFPf+nfx5f5b2pNfVM5bS/ZTZTAqHUkI0cQt++U4RhNMGBaKTts8psN4u9kx\n+4Hu1VNeEpJlyotQh1oXynq9Hmtr6/rMIv5B11Av+nZqyYnUAtm2RAhRa0UXK/jv6lgs9FpmjItE\np5NlKq5n9IA29I5oQULyOb788ajScYRQvddff52xY8cybtw4jhw5Un1/dnY2EydOrP7o378/P/zw\nA2vWrKFfv37V93/yyScKplfWybQCdh3JoF1rF3qGeysdp1F1CHLnX/d3x2Aw8n+f7yEp7bzSkYSg\n0ZbRS0tLo7KyskbPSU5ObqA09aexMt7c2YF9x7R88UM8LRwqcLa3uKHnSRvWneSru/rMGBgYWG+v\n1dx8tvYI5y+U88Ct7fH1ar6rW9eERqPhybGdOZN1ge+3n6Jdaxf6dG6pdCwhVGnv3r2kpqayfPly\nTp06xezZs1m+fDkAXl5eLFmyBICqqiomTpzIwIED2bBhAyNGjGDWrFlKRleFr9cfA2DSLaHNcnHF\nrqFePHNPF95Ztp+XFkTz5mNRMj1IKKrRCmVf35rNs0hOTlb9AXFjZ5w80or5K2P55WAhs+/vft3H\nSxvWneSrO3PI2BzsPpLB9kPptPNz4fZ+bZSOY1ZsrS2YfX93nvlgGx+uOETrFg74ycGbEH8THR3N\n4MGDAQgKCqKwsJDi4mLs7e2veNzatWsZOnQodnZ2SsRUpcSzxRxOzKVTsAcRbTyUjqOYPp1bUlJe\nxfyVh3nxs2jemn4T3m7ycyKU0fQ2ZmvCbu7ux+b9aUTHZRITn0mP8BZKRxJCmIHC4nI+WX0ES72W\nGeM6N5t5b/XJ18uBJ8dG8ubX+3jjy728+2Q/7GxubGSPEM1FXl4eYWFh1bddXV3Jzc39W6G8cuVK\nFi9eXH177969TJ48maqqKmbNmkX79u2v+T61GaUI6h2BZTKZ+DE6C4BBHR1VmxMapw3besIdUd58\ntyuL5+dv58nRgTjZyUjKxqL2fNB4IxVrXSjHx8fz1ltvkZ6ejl6vZ8OGDXz00Uc4OzvX9iXFdWh/\n31v5iXe38OmaI3Ro446ttRyoCSGu7bO1cZwvLufB28Jo5SlDrmsrqqMPo/q3Ye3WJN7/9iCz7+/e\nLIdHCnGjTKa/L0B66NAhAgMDq4vnjh074urqSv/+/Tl06BCzZs3ihx9+uObr1nSUIqh7dFNMfCap\nOaVERfjQv2e40nH+UWO2YWBgINa2x/l20wkW/pzOG49G4WRvpZp8taX2jGrPB42bsdaFcnh4ePVc\nE9F4fL0cuHNgW5ZvSmTxDwk8emdHtHJ1SAjxD3bFZrDjcDqh/q6M7CurXNfVfSNCSUo7z574LFZv\nSeKugW2VjiSEanh6epKXl1d9OycnBw+PK4cRb9269YqtRYOCgggKutQ3de7cmfz8fAwGAzpd090/\n+K827T0DwPgh7RROoi73DG1HSVkl63Yk8/LCaOZOi5ILRKJRyZKnZmjMoGB8vezZsCeVVxbtobC4\nXOlIQggVKiwu55M1sVjqtTwpQ67rhU6n5dmJXXBzsmbJ+qPEJuYqHUkI1YiKimLDhg0AJCQk4Onp\n+bdh13FxcYSEhFTfXrhwIT/++CMAiYmJuLq6Nqsiubi0kgPHc/Bxs8avhax9cDmNRsPkkeEM7taa\npLOFzFkQTXZ+idKxRDMihbIZsrTQ8cajNxEZ4smB4znMeG8rR0/LnnNCiD+ZTCY+WX2EwuIKJo5o\nT0sP++s/SdwQFwdrnp/UDa1Ww9tL95NTIAduQgBERkYSFhbGuHHjeO2115gzZw5r1qxh06ZN1Y/J\nzc3Fzc2t+vZtt93G8uXLmTBhAi+99BJz585VIrpiYuIzqTIY6dzGSekoqqTVapg+phN9O7fkeGoB\nj72zme+2JWGQfe1FI5DFvMyUk70Vcyb3ZPWWkyz9+Rj/+u8uJg0PZVT/NjIUWwjBtkPp7DqSQai/\nK7f1Ufd8I3MU4u/KQ7d34NM1R3jr6328+VgfLPRy7lmImTNnXnH78qvHwN/mH3t7ezfrqXw7DqcD\nSKF8DTqthpn3dqFrqBcLv4tn0boEth08y+NjOhPYUtpNNBz5q27GtFoNdw8KZu60KJztLfnyp6O8\nujiGoosVSkcTQijoXGEpn645grWljqfGR8qQ6wYyorc/A7q0IvHMeb5ef1TpOEIIM1N0sYLDibkE\ntXLCw/naC1U1dxqNhgFdfPlk1kAGdGlF0tlCnnp/G1/+mEBZRZXS8UQTJYVyExAe5M4HTw+gc7AH\n+49l8+R7Wzmekq90LCGEAkwmEx+uOMzF0koevC2MFu6y/2RD0Wg0TLuzIy097Phu2yn2H8tWOpIQ\nwoxEx2ViMJro07Gl0lHMhpO9FU/f04X/e6QXHs42rN6SxBPztsp6EaJBSKHcRDg7WPHyw72YMDyE\n/MJSnv94J9uP5F3/iUKIJuWXPakcPJ5DZDtPhvXyVzpOk2djpee5id3Q67T8538HOVdYqnQkIYSZ\n2Pn7sOubOkmhXFOR7TyZP3MAo/q3ITv/Ii98tptvt5zFYPz7lmRC1JYUyk2IVqth7OB2vDY1Ckc7\nS1bvyGTv0SylYwnR7CUmJjJ48GCWLl3aoO+TmXeRxevisbOx4ImxnWSP30YS2NKJySPDKLpYwXvf\nHJQDNSHEdZ2/UM6RpFzatXbBy9VW6ThmydpKz4O3hfHujH4E+DgSfbSAX6JTlI4lmhAplJugDm3c\neWVKb/Q6De//75Bc4RBCQSUlJbz66qtX7BvaEAxGE+9/e5CyCgNTR3XAzcmmQd9PXOmWqAB6hHlz\nJCmPVb8lKh1HCKFyu+MyMJrkanJ9aNPKmf97pBc2llqWrD9KwYUypSOJJkIK5SbKv4Ujo25qwYWS\nCuYtOyBXOIRQiKWlJQsXLsTT07NB32fd9lMcPZ1P74gW9Its1aDvJf5Oo9Hw5LjOuDvb8M2G4yQk\ny5Z9Qoh/9sdq1zd19FE4SdPg4mDNLT29uVhWxRc/JCgdRzQRsj1UExYV5srZfBPRcZms+DWR8UPa\nKR1JiCsUXazA0kKLtWXT7Yr0ej16/Y1/f2lpaVRWVtboPXbtO8rX65Owt9FzS1dnTp8+XdOYDS45\nOVnpCNdUX/nuGdCCj75L5s2vYnhubBvsrOvnZ1vt7Qfqz6j2fFC/GQMDZVs4tTp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qeP/b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\n",
            "text/plain": [
              "<Figure size 1209.6x432 with 6 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "8PB4I6Qafg9H",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## The model [WORK REQUIRED]\n",
        "This time we want to train a \"stateful\" RNN model, one that runs like a state machine with an internal state updated every time an input is processed. Stateful models are typically trained (unrolled) to predict the next element in a sequence, then used in a loop (without unrolling) to generate a sequence.\n",
        "1. Locate the inference function keras_prediction_run() below and check that at its core, it runs the model in a loop, piping outputs into inputs and output state into input state:<br/>\n",
        "```\n",
        "for i in range(n):\n",
        "    Yout = model.predict(Yout)\n",
        "```\n",
        "Notice that the output is passed around in the input explicitly. In Keras, the output state is passed around as the next input state automatically if RNN layers are declared with stateful=True\n",
        "1. Run the whole notebook as it is, with a dummy model that always predicts 1. Check that everything \"works\".\n",
        "1. Now implement a one layer RNN model:\n",
        " * Use stateful GRU cells [`tf.keras.layers.GRU(RNN_CELLSIZE, stateful=True, return_sequences=True)`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/GRU).\n",
        " * Make sure they all return full sequences with  [`return_sequences=True`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/GRU). The model should output a full sequence of length SEQLEN. The target is the input sequence shifted by one, effectively teaching the RNN to predict the next element of a sequence.\n",
        " * Do not forget to replicate the regression redout layer across all time steps with [`tf.keras.layers.TimeDistributed(tf.keras.layers.Dense(1))`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/TimeDistributed)\n",
        " * In Keras, stateful RNNs must be defined for a fixed batch size ([documentation](https://keras.io/getting-started/faq/#how-can-i-use-stateful-rnns)). On the first layer, in addition to input_shape, please specify batch_size=batchsize\n",
        " * Adjust shapes as needed with  [`Reshape`](https://www.tensorflow.org/api_docs/python/tf/keras/layers/Reshape) layers. Pen, paper and fresh brain cells <font size=\"+2\">🤯</font> still useful to follow the shapes. The shapes of inputs (a.k.a. \"features\") and targets (a.k.a. \"labels\") are displayed in the cell above this text.\n",
        "1. Add a second RNN layer.\n",
        " * The predictions might be starting to look good but the loss curve is pretty noisy.\n",
        "1. If we want to do stateful RNNs \"by the book\", training data should be arranged in batches in a special way so that RNN states after one batch are the correct input states for the sequences in the next batch (see [this illustration](https://docs.google.com/presentation/d/18MiZndRCOxB7g-TcCl2EZOElS5udVaCuxnGznLnmOlE/edit#slide=id.g139650d17f_0_584)). Correct data batching is already implemented: just use the rnn_minibatch_sequencer function in the training loop instead of dumb_minibatch_sequencer.\n",
        " * This should clean up the loss curve and improve predictions.\n",
        "1. Finally, add a learning rate schedule. In Keras, this is also done through a callback. Edit lr_schedule below and swap the constant learning rate for a decaying one (just uncomment it).\n",
        " * Now the RNN should be able to continue your curve accurately.\n",
        "1. (Optional) To do things really \"by the book\", states should also be reset when sequences are no longer continuous between batches, i.e. at every epoch. The reset_state callback defined below does that. Add it to the list of callbacks in model.fit and test.\n",
        " * It actually makes things slightly worse...\n",
        "1. (Optional) You can also add dropout regularization. Try dropout=DROPOUT on both your RNN layers.\n",
        " * Aaaarg 😫what happened ?\n",
        "1. (Optional) In Keras RNN layers, the dropout parameter is an input dropout. In the first RNN layer, you are dropping your input data ! That does not make sense. Remove the dropout fromn your first RNN layer. With dropout, you might need to train for longer. Try 10 epochs.\n",
        "\n",
        "\n",
        "![deep RNN schematic](https://googlecloudplatform.github.io/tensorflow-without-a-phd/images/RNN1.svg)\n",
        "<div style=\"text-align: right; font-family: monospace\">\n",
        "  X shape [BATCHSIZE, SEQLEN, 1]<br/>\n",
        "  Y shape [BATCHSIZE, SEQLEN, 1]<br/>\n",
        "  H shape [BATCHSIZE, RNN_CELLSIZE*NLAYERS]\n",
        "</div>\n",
        "In Keras layers, the batch dimension is implicit ! For a shape of [BATCHSIZE, SEQLEN, 1], you write [SEQLEN, 1]. In pure Tensorflow however, this is NOT the case."
      ]
    },
    {
      "metadata": {
        "id": "9ga_jncykosN",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def keras_model(batchsize, seqlen):\n",
        "\n",
        "  model = tf.keras.Sequential([\n",
        "      #\n",
        "      # YOUR MODEL HERE\n",
        "      # This is a dummy model that always predicts 1\n",
        "      #\n",
        "      tf.keras.layers.Lambda(lambda x: tf.ones([batchsize,seqlen]), input_shape=[seqlen,])\n",
        "  ])\n",
        "  \n",
        "  # keras does not have a pre-defined metric for Root Mean Square Error. Let's define one.\n",
        "  def rmse(y_true, y_pred): # Root Mean Squared Error\n",
        "    return tf.sqrt(tf.reduce_mean(tf.square(y_pred - y_true)))\n",
        "\n",
        "  # to finalize the model, specify the loss, the optimizer and metrics\n",
        "  model.compile(\n",
        "     loss = 'mean_squared_error',\n",
        "     optimizer = 'adam',\n",
        "     metrics = [rmse])\n",
        "  \n",
        "  return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "8311WBkinlGp",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Keras model callbacks\n",
        "\n",
        "# This callback records a per-step loss history instead of the average loss per\n",
        "# epoch that Keras normally reports. It allows you to see more problems.\n",
        "class LossHistory(tf.keras.callbacks.Callback):\n",
        "  def on_train_begin(self, logs={}):\n",
        "      self.history = {'loss': []}\n",
        "  def on_batch_end(self, batch, logs={}):\n",
        "      self.history['loss'].append(logs.get('loss'))\n",
        "      \n",
        "# This callback resets the RNN state at each epoch\n",
        "class ResetStateCallback(tf.keras.callbacks.Callback):\n",
        "  def on_epoch_begin(self, batch, logs={}):\n",
        "      self.model.reset_states()\n",
        "      print('reset state')\n",
        "\n",
        "reset_state = ResetStateCallback()\n",
        "      \n",
        "# learning rate decay callback\n",
        "def lr_schedule(epoch): return 0.01\n",
        "#def lr_schedule(epoch): return 0.0001 + 0.01 * math.pow(0.8, epoch)\n",
        "lr_decay = tf.keras.callbacks.LearningRateScheduler(lr_schedule, verbose=True)\n"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "XlKtnYjbfg9V",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## The training loop"
      ]
    },
    {
      "metadata": {
        "id": "Tcrj-e50yyuM",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Execute this cell to reset the model\n",
        "\n",
        "NB_EPOCHS = 8\n",
        "model = keras_model(BATCHSIZE, SEQLEN)\n",
        "\n",
        "# this prints a description of the model\n",
        "model.summary()\n",
        "\n",
        "display_lr(lr_schedule, NB_EPOCHS)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "sx7jowlUqhfu",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "You can re-execute this cell to continue training"
      ]
    },
    {
      "metadata": {
        "id": "UM0AIbCSfg9V",
        "colab_type": "code",
        "cellView": "both",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# You can re-execute this cell to continue training\n",
        "\n",
        "steps_per_epoch = (DATA_LEN-1) // SEQLEN // BATCHSIZE\n",
        "#generator = rnn_minibatch_sequencer(data, BATCHSIZE, SEQLEN, NB_EPOCHS)\n",
        "generator = dumb_minibatch_sequencer(data, BATCHSIZE, SEQLEN, NB_EPOCHS)\n",
        "full_history = LossHistory()\n",
        "history = model.fit_generator(generator,\n",
        "                              steps_per_epoch=steps_per_epoch,\n",
        "                              epochs=NB_EPOCHS,\n",
        "                              callbacks=[full_history, lr_decay, reset_state])"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "u_TxVHXPfg9Y",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "display_loss(history.history, full_history.history, NB_EPOCHS)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "xBmt2uUVfg9N",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "## Inference\n",
        "This is a generative model: run one trained RNN cell in a loop"
      ]
    },
    {
      "metadata": {
        "id": "UFalPiNOr2pt",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Inference from stateful model\n",
        "def keras_prediction_run(model, prime_data, run_length):\n",
        "  model.reset_states()\n",
        "  \n",
        "  data_len = prime_data.shape[0]\n",
        "  \n",
        "  #prime_data = np.expand_dims(prime_data, axis=0) # single batch with everything\n",
        "  prime_data = np.expand_dims(prime_data, axis=-1) # each sequence is of size 1\n",
        "  \n",
        "  # prime the state from data\n",
        "  for i in range(data_len - 1): # keep last sample to serve as the input sequence for predictions\n",
        "    model.predict(np.expand_dims(prime_data[i], axis=0))\n",
        "  \n",
        "  # prediction run\n",
        "  results = []\n",
        "  Yout = prime_data[-1] # start predicting from the last element of the prime_data sequence\n",
        "  for i in range(run_length+1):\n",
        "    Yout = model.predict(Yout)\n",
        "    results.append(Yout[0,0]) # Yout shape is [1,1] i.e one sequence of one element\n",
        "\n",
        "  return np.array(results)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "DBVi1tNofg9a",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "PRIMELEN=256\n",
        "RUNLEN=512\n",
        "OFFSET=20\n",
        "RMSELEN=128\n",
        "\n",
        "prime_data = data[OFFSET:OFFSET+PRIMELEN]\n",
        "\n",
        "# For inference, we need a single RNN cell (no unrolling)\n",
        "# Create a new model that takes a single sequence of a single value (i.e. just one RNN cell)\n",
        "inference_model = keras_model(1, 1)\n",
        "# Copy the trained weights into it\n",
        "inference_model.set_weights(model.get_weights())\n",
        "\n",
        "results = keras_prediction_run(inference_model, prime_data, RUNLEN)\n",
        "\n",
        "picture_this_8(data, prime_data, results, OFFSET, PRIMELEN, RUNLEN, RMSELEN)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "AQn6WA-pfg9c",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "Copyright 2018 Google LLC\n",
        "\n",
        "Licensed under the Apache License, Version 2.0 (the \"License\");\n",
        "you may not use this file except in compliance with the License.\n",
        "You may obtain a copy of the License at\n",
        "[http://www.apache.org/licenses/LICENSE-2.0](http://www.apache.org/licenses/LICENSE-2.0)\n",
        "Unless required by applicable law or agreed to in writing, software\n",
        "distributed under the License is distributed on an \"AS IS\" BASIS,\n",
        "WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.\n",
        "See the License for the specific language governing permissions and\n",
        "limitations under the License."
      ]
    }
  ]
}